Description Usage Arguments Details Value See Also Examples

For outcomes k in 0 to K, slope vector a, intercept vector c, and latent ability vector theta, the response probability function is

*P(pick=0|a,c,th) = 1-P(pick=1|a,c_1,th)*

*P(pick=k|a,c,th) = 1/(1+exp(-(a th + c_k))) - 1/(1+exp(-(a th + c_(k+1))))*

*P(pick=K|a,c,th) = 1/(1+exp(-(a th + c_K)))*

1 |

`outcomes` |
The number of choices available |

`factors` |
the number of factors |

`multidimensional` |
whether to use a multidimensional model.
Defaults to |

The graded response model was designed for a item with a series of
dependent parts where a higher score implies that easier parts of
the item were surmounted. If there is any chance your polytomous
item has independent parts then consider `rpf.nrm`

.
If your categories cannot cross then the graded response model
provides a little more information than the nominal model.
Stronger a priori assumptions offer provide more power at the cost
of flexibility.

an item model

Other response model:
`rpf.drm()`

,
`rpf.gpcmp()`

,
`rpf.grmp()`

,
`rpf.lmp()`

,
`rpf.mcm()`

,
`rpf.nrm()`

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